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课程大纲
Introduction to AI in Financial Crime
- Overview of fraud and AML in the digital finance era
- Traditional vs AI-based approaches
- Case studies from Mastercard, JPMorgan, and global banks
Machine Learning for Transaction Monitoring
- Supervised learning for risk scoring and classification
- Unsupervised learning for anomaly detection
- Real-time alert generation and stream processing
Graph Analytics and Network Risk Detection
- Modeling relationships between entities and transactions
- Detecting complex fraud schemes using graph AI
- Hands-on with Neo4j or similar tools
Natural Language Processing for AML
- Text mining in customer due diligence (CDD)
- Watchlist scanning using named entity recognition (NER)
- Prompt-based document review and suspicious activity reports (SARs)
Model Governance and Explainability
- Building explainable and auditable models
- Bias detection and mitigation in fraud detection algorithms
- Use of XAI techniques in compliance settings
Ethics, Regulation, and Model Risk
- Compliance with AML and KYC frameworks (e.g. FATF, FinCEN, EBA)
- AI ethics in surveillance and customer monitoring
- Reporting standards and regulatory auditability
Deployment Strategies and Future Trends
- Integrating AI models into existing transaction systems
- Feedback loops and model updating mechanisms
- Future of generative AI in fraud investigation and SAR automation
Summary and Next Steps
要求
- An understanding of fraud risk and AML procedures
- Experience with data analysis or compliance reporting
- Basic familiarity with Python or analytics platforms
Audience
- Fraud risk professionals
- AML compliance teams
- Security managers
14 小时
